k-means approach of Chan et al. (2011). Specifically, rather than using the average of subjects in a cluster as the cluster centroid, the k-medoids method groups data by finding the most representative subjects to serve as cluster centroids. Thus, the subject whose measures were the closest (having the least sum of distances) to the measures of all other subjects was selected as the first representative (Kaufman and Rousseeuw, 1990). Subsequently, subjects were selected to increase the within-cluster similarity until k representative subjects were chosen as the initial cluster centroids. Once the initialization was completed, k-medoids iteratively exchanged selected representatives with unselected ones to improve the within-cluster similarity.
